Dynamic bandwidth allocation with machine learning in dense WiFi network by Ricardo Alvarado, Bayron Opina, Johan Tellez, Vivian Triana

“…This document introduces the application of a machine learning-based prediction model to outline time intervals of congestion in a densely populated WiFi network employing dynamic load balancing. …”
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Hybrid extreme learning machine for real-time rate of penetration prediction by Abdelhamid Kenioua, Omar Djebili, Ammar Touati Brahim

“…Abstract This study presents a comparative analysis of hybrid Extreme Learning Machine (ELM) models optimized with metaheuristic algorithms Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), and Grey Wolf Optimizer (GWO) for real-time Rate of Penetration (ROP) prediction in drilling operations. …”
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Machine Learning Analysis of Arterial Oscillograms for Depression Level Diagnosis in Cardiovascular Health by Vladislav Kaverinsky, Dmytro Vakulenko, Liudmyla Vakulenko, Kyrylo Malakhov

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Global trends in machine learning applications for single-cell transcriptomics research by Xinyu Liu, Zhen Zhang, Chao Tan, Yinquan Ai, Hao Liu, Yuan Li, Jin Yang, Yongyan Song

“…Abstract Background Single-cell RNA sequencing (scRNA-seq) has revolutionized cellular heterogeneity analysis by decoding gene expression profiles at individual cell level, while machine learning (ML) has emerged as core computational tool for clustering analysis, dimensionality reduction modeling and developmental trajectory inference in single-cell transcriptomics(SCT). …”
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Optimising water use in copper flotation with the design of experiments and machine learning by Rachid El-Bacha, Abderrahim Salhi, Hafid Abderrafia, Souad Rabi

“…Through the design of experiments with the integration of machine learning, especially for the choice of the model, the optimal proportion was determined, which made it possible to achieve a metal recovery of more than 80% using a 50/50 mix of fresh and recycled water. …”
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Dynamic Surgical Prioritization: A Machine Learning and XAI-Based Strategy by Fabián Silva-Aravena, Jenny Morales, Manoj Jayabalan, Muhammad Ehsan Rana, Jimmy H. Gutiérrez-Bahamondes

“…Specifically, we employ the Light Gradient Boosting Machine (LightGBM) for predictive modeling, stochastic simulations to account for dynamic variables and competitive interactions, and SHapley Additive Explanations (SHAPs) to interpret model outputs at both the global and patient-specific levels. …”
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Psychotherapist remarks’ ML classifier: insights from LLM and topic modeling application by Alexander Vanin, Vadim Bolshev, Anastasia Panfilova

“…IntroductionThis paper addresses the growing intersection of machine learning (ML) and psychotherapy by developing a classification model for analyzing topics in therapist remarks. …”
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Advancing nearshore and onshore tsunami hazard approximation with machine learning surrogates by N. Ragu Ramalingam, K. Johnson, M. Pagani, M. Pagani, M. L. V. Martina

“…These simulation results are fit using a machine learning (ML)-based variational encoder–decoder model. …”
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Science Through Machine Learning: Quantification of Post‐Storm Thermospheric Cooling by Richard J. Licata, Piyush M. Mehta, Daniel R. Weimer, Douglas P. Drob, W. Kent Tobiska, Jean Yoshii

“…Abstract Machine learning (ML) models are universal function approximators and—if used correctly—can summarize the information content of observational data sets in a functional form for scientific and engineering applications. …”
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A novel bias-alleviated hybrid ensemble model based on over-sampling and post-processing for fair classification by Fang He, Xiaoxia Wu, Wenyu Zhang, Xiaoling Huang

“…With the rapid development of machine learning in the field of classification, the classification fairness has become the research emphasis second to prediction accuracy. …”
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Benchmarking In-Sensor Machine Learning Computing: An Extension to the MLCommons-Tiny Suite by Fabrizio Maria Aymone, Danilo Pietro Pau

“…This paper proposes a new benchmark specifically designed for in-sensor digital machine learning computing to meet an ultra-low embedded memory requirement. …”
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A machine learning model for predicting acute respiratory distress syndrome risk in patients with sepsis using circulating immune cell parameters: a retrospective study by Kaihuan Zhou, Lian Qin, Yin Chen, Hanming Gao, Yicong Ling, Qianqian Qin, Chenglin Mou, Tao Qin, Junyu Lu

“…This study aimed to develop a machine learning (ML) model to predict the risk of ARDS in patients with sepsis using circulating immune cell parameters and other physiological data. …”
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Measuring place rather than space: integrating landscape qualities for the urban–rural gradient using people’s perceptions and machine learning by Michal Switalski, Maarten Van Strien, Adrienne Grêt-Regamey

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Explainable AutoML models for predicting the strength of high-performance concrete using Optuna, SHAP and ensemble learning by Muhammad Salman Khan, Tianbo Peng, Tianbo Peng, Muhammad Adeel Khan, Asad Khan, Mahmood Ahmad, Mahmood Ahmad, Kamran Aziz, Mohanad Muayad Sabri Sabri, N. S. Abd EL-Gawaad

“…Early selection of optimal components and the development of reliable machine learning (ML) models can significantly reduce the time and cost associated with extensive experimentation. …”
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Unveiling geological complexity in the Serra Dourada Granite using self-organizing maps and hierarchical clustering: Insights for REE prospecting in the Goiás Tin Province, Brasíli... by Guilherme Ferreira da Silva, Marcos Vinícius Ferreira, Lucy Takehara Chemale, Igor Vasconcelos Santana, Nilson Francisquini Botelho

“…Our findings underscore the value of machine learning techniques, particularly SOM, in geoscientific data analysis. …”
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Development and validation of interpretable machine learning models for predicting AKI risk in patients treated with PD-1/PD-L1: a retrospective study by Wentong Liu, Kaiyue Ji, Qianwen Tang, Weiqi Xia, Wei Zhang, Lina Shao, Jiana Shi, Yukun Li, Ping Huang, Xiaolan Ye

“…This study aimed to develop and validate an interpretable machine learning (ML) model for early AKI prediction in patients undergoing PD-1/PD-L1 inhibitor therapy using a retrospective cohort design. …”
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Advances in ECG and PCG-based cardiovascular disease classification: a review of deep learning and machine learning methods by Asmaa Ameen, Ibrahim Eldesouky Fattoh, Tarek Abd El-Hafeez, Kareem Ahmed

“…Future researchers will benefit from this review on cardiovascular disorders by better understanding the Deep Learning and Machine Learning models now in the healthcare sector. …”
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Mapping the distribution of pine wilt disease based on selected machine learning algorithms and high-resolution Gaofen-2/7 remote sensing by Yifan Wang, Xiaocheng Zhou, Chongcheng Chen, Xiaoqin Wang, Hao Wu, Fanglin Tan, Ruijiao Wu

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Multi-model ensemble machine learning-based downscaling and projection of GRACE data reveals groundwater decline in Saudi Arabia throughout the 21st century by Arfan Arshad, Muhammad Shafeeque, Thanh Nhan Duc Tran, Ali Mirchi, Zaichen Xiang, Cenlin He, Amir AghaKouchak, Jessica Besnier, Md Masudur Rahman

“…This was accomplished by using multi-model ensemble machine learning (ML) approach leveraging Random Forest, CART, and Gradient Tree Boosting algorithms within Google Earth Engine (GEE). …”
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Chemometric and computational modeling of polysaccharide coated drugs for colonic drug delivery by Ahmad Khaleel AlOmari, Khaled Almansour

“…The Raman method was used for collection of spectral data which were then used as inputs to the ML models for estimation of drug release. For ML modeling, we examined the predictive accuracy of three machine learning models—Elastic Net (EN), Group Ridge Regression (GRR), and Multilayer Perceptron (MLP)—for forecasting the release behavior of samples. …”
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